Related work on cdevcr has so far only scored predictions with the CoNLL F1 [1]} metric (and its constituent parts MUC [2]}, CEAF\(_e\)  [3]} and B\(^3\)  [4]}). We additionally score predictions with the LEA metric [5]}. LEA is a link-based metric which, in contrast to other metrics, takes the size of coreference clusters into account. The metric penalizes incorrect merges between two large clusters more than incorrect merges of mentions from two singleton clusters. As we have shown that cluster sizes in cdevcr corpora vary considerably (see fig:clustersizedistribution) this is a particularly important property. LEA was furthermore shown to be more discriminative than the established metrics MUC, CEAF\(_e\) , B\(^3\)  and CoNLL F1 [5]}.
